Overall Architecture:
As shown in Fig. REF , the proposed DURRNet is designed in a multi-resolution fashion. There are \(S\)  scales of DURRLayers to effectively exploit information at different scales for separating the input image into a transmission image and a reflection image. Each scale consists of \(K\)  DURRLayers. At the lowest scale, the initial transmission image \(\mathbf {T}_S\) , reflection image \(\mathbf {R}_S\)  and features \(\mathbf {z}_{T,S},\mathbf {z}_{R,S}\)  are initialized based on the down-sampled input image and the the hyper-column feature [1]}, [2]} of the input image using bilinear interpolation by a factor \(2^{S-1}\) , respectively. At an upper scale, the transmission and reflection images are initialized based on the 2 times up-sampled version estimated from its lower scale, and the features are initialized based on the down-sampled hyper-column feature of the input image and the up-sampled feature estimated from the lower scale. The multi-scale architecture performs image separation in a coarse-to-fine manner and can therefore effectively combine information from different scales.
